@inproceedings{43c6c9ad86d445a0a111ffd50f41f784,
title = "Adaptive M-Estimation for Robust Cubature Kalman Filtering",
abstract = "As a l1/l2 norms-based estimation method, Huber's M- estimation has provided an efficient method to deal with measurement outliers for robust filtering, which has been applied to the cubature Kalman filter (CKF), namely Huber's M-estimation based robust CKF (HCKF) and its square-root version (HSCKF). To further handle abnormal measurement noise, an adaptive method is proposed in this paper to adjust the measurement noise covariance used in the Huber's M-estimation approach based on the difference between actual and theoretical innovation covariance, leading to adaptive HCKF (AHCKF) and adaptive HSCKF (AHCKF). Simulation results on a typical target tracking model have demonstrated their advantages over existing approaches in terms of estimate accuracy, outlier-robustness and reliability.",
keywords = "Kalman filter, M-estimation, Point estimator, Robust estimation, Target tracking",
author = "Changliang Zhang and Ruirui Zhi and Tiancheng Li and Juan Corchado",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 6th Conference of the Sensor Signal Processing for Defence, SSPD 2016 ; Conference date: 22-09-2016 Through 23-09-2016",
year = "2016",
month = oct,
day = "13",
doi = "10.1109/SSPD.2016.7590586",
language = "英语",
series = "2016 Sensor Signal Processing for Defence, SSPD 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 Sensor Signal Processing for Defence, SSPD 2016",
}